app.py

from fastai.vision.all import *
import gradio as gr
#import torch
#from diffusers import FluxPipeline
#from huggingface_hub import login
#login()

#pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
#pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power

#prompt = "A cat holding a sign that says hello world"
#image = pipe(
  #  prompt,
   # height=1024,
   # width=1024,
   # guidance_scale=3.5,
   # num_inference_steps=50,
    #max_sequence_length=512,
   # generator=torch.Generator("cpu").manual_seed(0)
#).images[0]
#image.save("flux-dev.png")


learn = load_learner('export.pkl')

categories = ('balsamroot', 'bladderpod', 'blazing star', 'bristlecone pine flowers', 'brittlebrush')
def classify_image(img):
    pred, idx, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))



image=gr.Image(height = 192, width = 192)
label = gr.Label()
examples = ['https://www.deserthorizonnursery.com/wp-content/uploads/2024/03/Brittlebush-Encelia-Farinosa-desert-horizon-nursery.jpg','https://cdn.mos.cms.futurecdn.net/VJE7gSuQ9KWbkqEsWgX5zS.jpg']
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)